In future, thanks to the advancements taking place in the deep learning field, it could be possible that complex problems would be automatically solved using the surrounding factors and learned experiences. Imagine your brain continuously exchanging information with electronic components and helping them get better! This isn’t a plot of some science fiction movie; such scenarios could witness the daylight with the aid of neuromorphic, or brain-inspired, computing and AI.

The silicon giant Intel has been working on the idea of comparing machines with the human brain. As a result, Intel Labs has developed the first self-learning and neuromorphic chip called Loihi.

Loihi’s digital circuits are designed to mimic the brain functions and learn how to operate using feedback from the surrounding environment. The chip uses data to learn, make inferences, and improve its intelligence over the time–one doesn’t need to train Loihi traditionally.

By trying to work like the brain, Loihi makes machine learning faster and more efficient. By observing spikes and plastic synapses that can be modulated based on timing, it learns how neurons communicate and learn.

The latest Loihi test chip, which is still under testing, offers flexible on-chip learning. The researchers have also demonstrated learning at a 1 million times improved rate compared to other typical spiking neural nets. It’s few highlights are:

Based on Intel’s 14nm process

Total 130,000 neurons and 130 million synapses

Each neuromorphic core has a learning engine

Fully asynchronous neuromorphic many core mesh

The potential applications of Intel Loihi chip would be in the automotive, industrial, and personal robotics field. There, an application would be able to learn continuously in an unstructured environment. Moreover, Loihi is also 1,000 times more energy efficient when compared to normal computing needed for typical training systems.

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